Custom partitioning of a data stream

ABSTRACT

Techniques for partitioning data streams are provided. In some examples, a query for processing at least a portion of a data stream may be identified. The data stream may be associated with a user. Additionally, in some examples, code identifying an attribute of the identified stream may be received. The code may be capable of configuring the query based at least in part on the attribute. Further, in some aspects, the code may be configured to partition the data stream into at least a sub-stream based at least in part on the attribute.

BACKGROUND

Event processing applications can be configured to process very largeamounts of streaming data from many disparate sources. In some cases,the streaming data may be associated with multiple different attributes.For example, a stream of customer data associated with accessing networkcontent of a company may include Internet Protocol (IP) addresses and/orgeographic location information associated with the customers. Often,the data streams are received at very high rates and include nearlyunmanageable amounts of information. With a wide variety of inputstreams and data parameters, and the ever increasing speed of and volumeof information received, it can become burdensome to manage such datastreams.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is set forth with reference to the accompanyingfigures. In the figures, the left-most digit(s) of a reference numberidentifies the FIG. in which the reference number first appears. The useof the same reference numbers in different FIGS. indicates similar oridentical items.

FIG. 1 is a simplified block diagram illustrating an examplearchitecture for managing custom partitioning of data streams asdescribed herein, according to at least one example.

FIG. 2 is a simplified block diagram illustrating at least some featuresof an event processing engine capable of handling continuous streams ofdata as described herein, according to at least one example.

FIG. 3 is a simplified block diagram illustrating at least some featuresof a custom configuration service configured to implement the custompartitioning of data streams described herein, according to at least oneexample.

FIG. 4 is a simplified flow diagram illustrating at least sometechniques for the custom partitioning of data streams described herein,according to at least one example.

FIG. 5 is a simplified flow diagram illustrating at least one processfor implementing the custom partitioning of data streams describedherein, according to at least one example.

FIG. 6 is a simplified flow diagram illustrating at least one processfor implementing the custom partitioning of data streams describedherein, according to at least one example.

FIG. 7 depicts a simplified diagram of a distributed system forimplementing some of the examples described herein, according to atleast one example.

FIG. 8 is a simplified block diagram of components of a systemenvironment by which services provided by the components of anembodiment system may be offered as cloud services, in accordance withsome of the examples described herein, according to at least oneexample.

FIG. 9 illustrates an exemplary computer system, in which variousembodiments of the present disclosure may be implemented in accordingwith some of the examples described herein, according to at least oneexample.

BRIEF SUMMARY

In the following description, various embodiments will be described. Forpurposes of explanation, specific configurations and details are setforth in order to provide a thorough understanding of the embodiments.However, it will also be apparent to one skilled in the art that theembodiments may be practiced without the specific details. Furthermore,well-known features may be omitted or simplified in order not to obscurethe embodiment being described.

According to one embodiment, a system may be configured with a memoryand a processor. The processor may be configured to execute instructionsstored on the memory to identify a data stream associated with the user.The processor may also be configured to execute the instructions toreceive a query for processing at least a portion of the identified datastream and receive, from the user, code identifying an attribute of theidentified data stream, the code capable of configuring the query basedat least in part on the attribute. In some aspects, the process may beconfigured to execute the instructions to associate, by the computersystem, the code with the query and configure the query to partition thedata stream based at least in part on the attribute after the code isassociated with the query. The instructions may also be executed topartition the data stream into a plurality of portions based at least inpart on the attribute, provide a sub-stream for at least one of theplurality of portions to the query, and provide results of the querybased at least in part on the provided sub-stream.

In some examples, the code comprises an object-oriented class based atleast in part on a contract provided to the user, and configuring thequery comprises tying the query to the object-oriented class. Theinstructions may also be executed to receive an event of the data streambased at least in part on the query, provide the event to theobject-oriented class, and receive an output from the object-orientedclass. The data stream may be partitioned based at least in part on theoutput from the object-oriented class. The attribute may comprise atleast one of a first identifier of a computing device of a customer ofthe user, a second identifier of an item associated with the user, or athird identifier of an item attribute associated with the item. Theidentifier of the computing device of the customer may indicate anInternet Protocol address of the computing device of the customer or ageographic region in which the computing device of the customer islocated.

According to another embodiment, a computer-readable medium may includeinstructions that, when executed, configure a computer processor toidentify a query for processing at least a portion of a data streamassociated with a user. The instructions may further configure theprocessor to receive, from the user, code identifying an attribute ofthe identified data stream, the code capable of configuring the querybased at least in part on the attribute. The instructions may alsoconfigure the processor to configure the query to process one or moresub-streams of the data stream based at least in part on the attribute.

In some examples, the plurality of instructions may further compriseinstructions that cause the one or more processors to process aplurality of the one or more sub-streams in parallel. The code maycomprise a java class based at least in part on a java contract providedto the user. The plurality of instructions may further compriseinstructions that cause the one or more processors to receive an eventof the data stream based at least in part on the query and provide theevent to the java class. The plurality of instructions may furthercomprise instructions that cause the one or more processors to implementthe java class to process the event based at least in part on theattribute and receive an output from the java class. In some examples,the output from the java class may be at least one of the one or moresub-streams. The attribute may be specified by the user prior to receiptof the code. In some examples, at least one sub-stream of the one ormore sub-streams may be generated based at least in part on a hashingfunction performed on the at least a portion of the data stream.

According to another embodiment, a method may be executed by a computersystem to at least identify, by a computing system, a data streamassociated with a user. The method may also receive a query forprocessing at least a portion of the identified data stream. The methodmay also receive, from the user, code identifying an attribute of theidentified data stream, the code capable of configuring the query basedat least in part on the attribute. The method may also configure thecode to partition the data stream into at least one sub-stream based atleast in part on the attribute and process the at least one sub-streamusing the received query.

In some cases, the method may also associate the code with the query.The at least one sub-stream may be processed, using the received query,in parallel with at least a second sub-stream received as an output fromthe code. The method may also receive an event of the data stream basedat least in part on the query and provide the event to a java class thatcorresponds to the received code. In some instances, the method mayimplement the java class to process the event based at least in part onthe attribute and receive an output from the java class that identifiesthe at least one sub-stream. The data stream may be partitioned based atleast in part on the java class. The sub-stream may be generated basedat least in part on a hashing function performed on at least one of theplurality of portions. Additionally, the attribute may comprise at leastone of an Internet Protocol address associated with a customer of theuser or a geographic region associated with the customer of the user.

The foregoing, together with other features and embodiments will becomemore apparent upon referring to the following specification, claims, andaccompanying drawings.

DETAILED DESCRIPTION

In the following description, various embodiments will be described. Forpurposes of explanation, specific configurations and details are setforth in order to provide a thorough understanding of the embodiments.However, it will also be apparent to one skilled in the art that theembodiments may be practiced without the specific details. Furthermore,well-known features may be omitted or simplified in order not to obscurethe embodiment being described.

Embodiments of the present disclosure are directed to, among otherthings, providing a custom (e.g., customizable) mechanism forpartitioning streaming data. In some examples, this custom mechanism maybe provided within an event processing and/or business intelligenceframework. For example, an event processing framework may be configuredto process streaming data from one or more sources (e.g., third-partystreaming sources or the like) and manage, store, and/or output the datain one or more formats (e.g., as a stream and/or one or moresub-streams). As noted, a customizable mechanism may be provided thatenables one or more users, customers, etc., to configure the eventprocessing framework (e.g., utilizing an event processing engine) topartition one or more incoming streams based at least in part on customparameters.

In one non-limiting example, a customer may develop and/or provide aquery (e.g., a continuous query) for querying data from an incoming datastream. The stream may be received by a service provider that processesthe stream using the query for the customer; however, in other examples,the customer may have the data processed using the query without the useof a service provider. In either event, the customer may also providesome configuration information that identifies one or more attributes ofthe data stream for partitioning. For example, the data of the datastream may include any number and/or type of attributes (e.g., an IPaddress associated with the data, a geographic location (or region)associated with the data, a user identifier (ID) associated the data,etc.). Based on the configuration information, the service provider maybe configured to identify the attribute listed in the configurationinformation, extract or otherwise collect events associated with theidentified attribute, and provide (e.g., as an output data stream suchas, but not limited to, a sub-stream) the extracted and/or collectedevents associated with that attribute. The output sub-streams may beprocessed using one or more queries including post-processing such as,but not limited to, filtering, aggregating, etc. Thus, after the codepartitions the event stream into sub-streams, downstream queryprocessing may occur. In this way, a customer (also referred to hereinas a user) may configure the service provider to partition the incomingdata streams based at least in part on selected event attributes.Additionally, the user may fully customize the partitioning and/orprovide code to fully customize the partitioning of the incoming datastreams.

In some examples, the service provider may be configured to supportcontinuous query language (CQL) queries (also referred to as “querystatements”) on one or more data streams. Additionally, in someexamples, mechanisms for supporting the CQL queries may also enableconfiguration of scripting code executed by the service providers (e.g.,JavaScript or the like). This code may be configured, generated,managed, updated, and/or otherwise manipulated by a user, administrator,or other entity associated with the event data (e.g., business eventdata).

In general, a continuous data stream (also referred to as an eventstream) may include a stream of data or events that may be continuous orunbounded in nature with no explicit end. Logically, an event or datastream may be a sequence of data elements (also referred to as events),each data element having an associated timestamp. A continuous eventstream may be logically represented as a bag or set of elements (s, T),where “s” represents the data portion, and “T” is in the time domain.The “s” portion is generally referred to as a tuple or event. An eventstream may thus be a sequence of time-stamped tuples or events.

In some aspects, the timestamps associated with events in a stream mayequate to a clock time. In other examples, however, the time associatedwith events in an event stream may be defined by the application domainand may not correspond to clock time but may, for example, berepresented by sequence numbers instead. Accordingly, the timeinformation associated with an event in an event stream may berepresented by a number, a timestamp, or any other information thatrepresents a notion of time. For a system receiving an input eventstream, the events arrive at the system in the order of increasingtimestamps. There could be more than one event with the same timestamp.

In some examples, an event in an event stream may represent anoccurrence of some worldly event (e.g., when a temperature sensorchanged value to a new value, when the price of a stock symbol changed)and the time information associated with the event may indicate when theworldly event represented by the data stream event occurred.Additionally, attributes associated with each event may indicateparticular (e.g., relevant) information (e.g., stored as metadata) aboutthe worldly event such as, but not limited to, a user and/or itemassociated with the worldly event (e.g., a purchaser ID, a seller ID, aproduct ID, a price of the item, a location of the user, etc.), weatherat a location during the worldly event, or the like.

For events received via an event stream, the time information associatedwith an event may be used to ensure that the events in the event streamarrive in the order of increasing timestamp values. This may enableevents received in the event stream to be ordered based upon theirassociated time information. In order to enable this ordering,timestamps may be associated with events in an event stream in anon-decreasing manner such that a later-generated event has a latertimestamp than an earlier-generated event. As another example, ifsequence numbers are being used as time information, then the sequencenumber associated with a later-generated event may be greater than thesequence number associated with an earlier-generated event. In someexamples, multiple events may be associated with the same timestamp orsequence number, for example, when the worldly events represented by thedata stream events occur at the same time. Events belonging to the sameevent stream may generally be processed in the order imposed on theevents by the associated time information, with earlier events beingprocessed prior to later events.

The time information (e.g., timestamps) associated with an event in anevent stream may be set by the source of the stream or alternatively maybe set by the system receiving the stream. For example, in certainembodiments, a heartbeat may be maintained on a system receiving anevent stream, and the time associated with an event may be based upon atime of arrival of the event at the system as measured by the heartbeat.It is possible for two events in an event stream to have the same timeinformation. It is to be noted that while timestamp ordering requirementis specific to one event stream, events of different streams could bearbitrarily interleaved.

An event stream may have an associated schema “S,” the schema comprisingtime information and a set of one or more named attributes. All eventsthat belong to a particular event stream conform to the schemaassociated with that particular event stream. Accordingly, for an eventstream (s, T), the event stream may have a schema ‘S’ as (<time_stamp>,<attribute(s)>), where <attributes> represents the data portion of theschema and can comprise one or more attributes. For example, the schemafor a stock ticker event stream may comprise attributes <stock symbol>,and <stock price>. Each event received via such a stream will have atime stamp and one or more attributes. For example, the stock tickerevent stream may receive the following events and associated timestamps:

... (<timestamp_N>, <NVDA,4>) (<timestamp_N+1>, <ORCL,62>)(<timestamp_N+2>, <PCAR,38>) (<timestamp_N+3>, <SPOT,53>)(<timestamp_N+4>, <PDCO,44>) (<timestamp_N+5>, <PTEN,50>) ...In the above stream, for stream element (<timestamp_N+1>, <ORCL,62>),the event is <ORCL62> with attributes “stock_symbol” and “stock_value.”The timestamp associated with the stream element is “timestamp_N+1.” Acontinuous event stream is thus a flow of events, each event having aseries (in some examples, the same series) of attributes.

As noted, a stream may be the principle source of data that CQL queriesmay act on. A stream S may be a bag (also referred to as a “multi-set”)of elements (s, T), where “s” is in the schema of S and “T” is in thetime domain. Additionally, stream elements may be tuple-timestamp pairs,which can be represented as a sequence of timestamped tuple insertions.In other words, a stream may be a sequence of timestamped tuples. Insome cases, there may be more than one tuple with the same timestamp.And, the tuples of an input stream may be requested to arrive at thesystem in order of increasing timestamps. Further, as used herein, acontinuous query may generally be capable of processing data of (i.e.,queried against) a stream.

In the following description, for the purposes of explanation, specificdetails are set forth in order to provide a thorough understanding ofembodiments of the disclosure. However, it will be apparent that variousembodiments may be practiced without these specific details. The figuresand description are not intended to be restrictive.

Systems depicted in some of the figures may be provided in variousconfigurations. In some embodiments, the systems may be configured as adistributed system where one or more components of the system aredistributed across one or more networks in a cloud computing system.

FIG. 1 depicts a simplified example system or architecture 100 in whichtechniques for managing configurable/custom partitions of a data streammay be implemented. In architecture 100, one or more users 102 (e.g.,account holders) may utilize user computing devices 104(1)-(N)(collectively, “user devices 104”) to access one or more serviceprovider computers 106 via one or more networks 108. In some aspects,the service provider computers 106 may also be in communication with oneor more streaming data source computers 110 and/or one or more databases112 via the networks 108. For example, the users 102 may utilize theservice provider computers 106 to access or otherwise manage data of thestreaming data source computers 110 and/or the databases 112 (e.g.,queries may be used to process data of either or both of the streamingdata source computers 110 or the databases 112). The databases 112 maybe relational databases, SQL servers, or the like and may, in someexamples, manage historical data, event data, relations, archivedrelations, or the like on behalf of the users 102. Additionally, thedatabases 112 may receive or otherwise store data provided by thestreaming data source computers 110. In some examples, the users 102 mayutilize the user devices 104 to interact with the service providercomputers 106 by providing queries (also referred to as “querystatements”) or other requests for data (e.g., historical event data,streaming event data, etc.). Such queries or requests may then beexecuted by the service provider computers 106 to process data of thedatabases 112 and/or incoming data from the streaming data sourcecomputers 110 (e.g., continuously querying incoming data as the incomingdata is pushed to the query). Further, in some examples, the streamingdata source computers 110 and/or the databases 112 may be part of anintegrated, distributed environment associated with the service providercomputers 106.

In some examples, the networks 108 may include any one or a combinationof multiple different types of networks, such as cable networks, theInternet, wireless networks, cellular networks, intranet systems, and/orother private and/or public networks. While the illustrated examplerepresents the users 102 accessing the service provider computers 106over the networks 108, the described techniques may equally apply ininstances where the users 102 interact with one or more service providercomputers 106 via the one or more user devices 104 over a landlinephone, via a kiosk, or in any other manner. It is also noted that thedescribed techniques may apply in other client/server arrangements(e.g., set-top boxes, etc.), as well as in non-client/serverarrangements (e.g., locally stored applications, etc.).

The user devices 104 may be any type of computing device such as, butnot limited to, a mobile phone, a smart phone, a personal digitalassistant (PDA), a laptop computer, a desktop computer, a thin-clientdevice, a tablet PC, etc. In some examples, the user devices 104 may bein communication with the service provider computers 106 via thenetworks 108, or via other network connections. The user devices 104 mayalso be configured to provide one or more queries or query statementsfor requesting data of the databases 112 (or other data stores) to beprocessed. The user devices 104 may also be configured to provide codefor configuring the service provider computers 106 to partition incomingevent data based at least in part on the code. For example, the usercomputers 104 may provide an extensible hypertext markup language (XML),a user-configured java class, or the like that the service providercomputers 106 can execute in order to implement a JavaScript forpartitioning the incoming streams as appropriate.

In some aspects, the service provider computers 106 may also be any typeof computing devices such as, but not limited to, mobile, desktop,thin-client, and/or cloud computing devices, such as servers. In someexamples, the service provider computers 106 may be in communicationwith the user devices 104 via the networks 108, or via other networkconnections. The service provider computers 106 may include one or moreservers, perhaps arranged in a cluster, as a server farm, or asindividual servers not associated with one another. These servers may beconfigured to perform or otherwise host features described hereinincluding, but not limited to, the management custom partitions forevent streams described herein. Additionally, in some aspects, theservice provider computers 106 may be configured as part of anintegrated, distributed computing environment that includes thestreaming data source computers 110 and/or the databases 112.

In one illustrative configuration, the service provider computers 106may include at least one memory 136 and one or more processing units (orprocessor(s)) 138. The processor(s) 138 may be implemented asappropriate in hardware, computer-executable instructions, firmware, orcombinations thereof. Computer-executable instruction or firmwareimplementations of the processor(s) 138 may include computer-executableor machine-executable instructions written in any suitable programminglanguage to perform the various functions described.

The memory 136 may store program instructions that are loadable andexecutable on the processor(s) 138, as well as data generated during theexecution of these programs. Depending on the configuration and type ofservice provider computers 106, the memory 136 may be volatile (such asrandom access memory (RAM)) and/or non-volatile (such as read-onlymemory (ROM), flash memory, etc.). The service provider computers 106 orservers may also include additional storage 140, which may includeremovable storage and/or non-removable storage. The additional storage140 may include, but is not limited to, magnetic storage, optical disks,and/or tape storage. The disk drives and their associatedcomputer-readable media may provide non-volatile storage ofcomputer-readable instructions, data structures, program modules, andother data for the computing devices. In some implementations, thememory 136 may include multiple different types of memory, such asstatic random access memory (SRAM), dynamic random access memory (DRAM),or ROM.

The memory 136, the additional storage 140, both removable andnon-removable, are all examples of computer-readable storage media (andmay be non-transitory). For example, computer-readable storage media mayinclude volatile or non-volatile, removable or non-removable mediaimplemented in any method or technology for storage of information suchas computer-readable instructions, data structures, program modules, orother data. The memory 136 and the additional storage 140 are allexamples of computer storage media.

Alternatively, computer-readable communication media may includecomputer-readable instructions, program modules, or other datatransmitted within a data signal, such as a carrier wave, or othertransmission. However, as used herein, computer-readable storage mediadoes not include computer-readable communication media.

The service provider computers 106 may also contain communicationsconnection(s) 142 that allow the service provider computers 106 tocommunicate with a stored database, another computing device or server,user terminals, and/or other devices on the networks 108. The serviceprovider computers 106 may also include input/output (I/O) device(s)144, such as a keyboard, a mouse, a pen, a voice input device, a touchinput device, a display, one or more speakers, a printer, etc.

Turning to the contents of the memory 136 in more detail, the memory 136may include an operating system 145 and one or more application programsor services for implementing the features disclosed herein including atleast a custom configuration service 146. In some examples, the customconfiguration service 146 may be implemented by one or more applicationprograms or features including, but not limited to, user applicationmodule 148, a contract module 149, and/or a configurable partitionmodule 150. As used herein, modules may refer to programming modulesexecuted by servers or clusters of servers that are part of a service(e.g., the custom configuration service 146). In this particularcontext, the modules may be executed by the servers or clusters ofservers that are part of the service provider computers 106.

In some examples, the user application module 148 may be configured to,receive, identify, generate, or otherwise manage event streamsassociated with the users 102. For example, a user 102 may own abusiness (e.g., an electronic marketplace or the like) that providesevent streams via one of the streaming data source computers 110. Assuch, the user 102 may rely on the service provider computers 106 tomanage the data of the event streams. Additionally, in some examples,the users 102 may provide one or more queries (e.g., query 154) forprocessing incoming event streams associated with their business. Assuch, based at least in part on the received query 154, and the receivedevent streams, the service provider computers 106 may provide results(e.g., in a user interface (UI) or the like) of the query 154 via theuser application module 148.

In some examples, the contract module 149 may be configured to provide asoftware development kit (SDK) or the like to the users 102 forconfiguring the partitioning of event streams. Additionally, thecontract module 149 may be configured to receive, store, and/or manageone or more custom contracts (e.g., from the users 102) that aredeveloped based at least in part on the SDK or other agreement. Thecustom contracts may include configuration information 155 thatidentifies the custom partitions requested by the user 102. In somecases, though, the contract module 149 may be part of and/or work inconjunction with the user application module 148 to provide a UI forconfiguring the custom partitions without the user 102 writing any code.As such, the UI may provide drop down lists, check boxes, radio buttons,text fields, and/or any other way for the user to indicate theconfiguration information 155 through the UI.

Additionally, in some examples, the configurable partition module 150may be configured to receive the configuration information 155 from atleast the contract module 149 and the query 154 from the userapplication module 148. The configurable partition module 150 may alsobe configured to configure 156 the query 154 based at least in part onthe configuration information 155 and the query 154. Once the query 154is configured 156, the configurable partition module 150 may partition157 one or more incoming data streams and generate 158 one or moresub-streams. The generated 158 sub-streams may then be provided (e.g.,pushed) to the query 154 for processing, and then subsequently providedto the user devices 104 as requested.

Additional types of computer storage media (which may also benon-transitory) that may be present in the service provider computers106 and/or user devices 104 may include, but are not limited to,programmable random access memory (PRAM), SRAM, DRAM, RAM, ROM,electrically erasable programmable read-only memory (EEPROM), flashmemory or other memory technology, compact disc read-only memory(CD-ROM), digital versatile discs (DVD) or other optical storage,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or any other medium which can be used to storethe desired information and which can be accessed by the serviceprovider computers 106 and/or user devices 104. Combinations of any ofthe above should also be included within the scope of computer-readablestorage media.

FIG. 2 depicts a simplified high level diagram of an event processingsystem 200 that may incorporate an embodiment of the present disclosure.Event processing system 200 may comprise one or more event sources 204,206, 208, an event processing server (EPS) 202 that is configured toprovide an environment for processing event streams, and one or moreevent sinks 210, 212. The event sources generate event streams that arereceived by EPS 202. EPS 202 may receive one or more event streams fromone or more event sources 204, 206, 208. For example, as shown in FIG.2, the EPS 202 receives an input event stream 214 from event source 204,a second input event stream 216 from event source 206, and a third eventstream 218 from event source 208. One or more event processingapplications 220, 222, and 224 may be deployed on and be executed by theEPS 202. An event processing application (e.g., event application 220)executed by the EPS 202 may be configured to listen to one or more inputevent streams, process the events received via the one or more eventstreams based upon processing logic that selects one or more events fromthe input event streams as notable events. The notable events may thenbe sent to the one or more event sinks 210, 212 in the form of one ormore output event streams. For example, in FIG. 2, the EPS 202 outputsan output event stream 226 to event sink 210, and a second output eventstream 228 to event sink 212. In certain embodiments, event sources,event processing applications, and event sinks are decoupled from eachother such that one can add or remove any of these components withoutcausing changes to the other components.

In one embodiment, the EPS 202 may be implemented as a Java servercomprising a lightweight Java application container, such as one basedupon Equinox OSGi, with shared services. In some embodiments, the EPS202 may support ultra-high throughput and microsecond latency forprocessing events, for example, by using JRockit Real Time. The EPS 202may also provide a development platform (e.g., a complete real timeend-to-end Java Event-Driven Architecture (EDA) development platform)including tools (e.g., Oracle CEP Visualizer and Oracle CEP IDE) fordeveloping event processing applications.

At least one of the event processing applications 220, 222, 224 isconfigured to listen to one or more input event streams 214, 216, 218,execute logic (e.g., a query) for selecting one or more notable eventsfrom the one or more input event streams, and output the selectednotable events to one or more event sinks 210, 212 via the one or moreoutput event streams 226, 228. FIG. 2 provides a drilldown for one suchevent processing application 220 that may in some examples, beconfigured to perform the features noted above with respect to thecustom configuration service 146. As shown in FIG. 2, the eventprocessing application 220 is configured to listen to input event stream218, execute a query via a CQL engine/CQ service comprising logic forselecting one or more notable events from input event stream 218, andoutput the selected notable events via output event stream 228 to eventsink 212. Examples of event sources include, without limitation, anadapter (e.g., JMS, HTTP, and file), a channel, a processor, a table, acache, or the like. Examples of event sinks include, without limitation,an adapter (e.g., JMS, HTTP, and file), a channel, a processor, a cache,and the like.

Although event processing application 220 in FIG. 2 is shown aslistening to one input stream and outputting selected events via oneoutput stream, this is not intended to be limiting. In alternativeembodiments, an event processing application may be configured to listento multiple input streams received from one or more event sources,select events from the monitored streams, and output the selected eventsvia one or more output event streams to one or more event sinks. Thesame query can be associated with more than one event sink and withdifferent types of event sinks.

Due to its unbounded nature, the amount of data that is received via anevent stream is generally very large. Consequently, it is generallyimpractical and undesirable to store or archive all the data forquerying purposes. The processing of event streams requires processingof the events in real time as the events are received by the EPS 202without having to store all the received event data. Accordingly, theEPS 202 may provide a special querying mechanism that enables processingof events to be performed as the events are received by the EPS 202without having to store all the received events.

Event-driven applications may be rule-driven and these rules may beexpressed in the form of continuous queries that are used to processinput streams. A continuous query may comprise instructions (e.g.,business logic) that identify the processing to be performed forreceived events including what events are to be selected as notableevents and output as results of the query processing. Continuous queriesmay be persisted to a data store and used for processing input streamsof events and generating output streams of events. Continuous queriestypically perform filtering and aggregation functions to discover andextract notable events from the input event streams. As a result, thenumber of outbound events in an output event stream is generally muchlower than the number of events in the input event stream from which theevents are selected.

Unlike a SQL query that is run once on a finite data set, a continuousquery that has been registered by an application with the EPS 202 for aparticular event stream may be executed each time that an event isreceived in that event stream. As part of the continuous queryexecution, the EPS 202 may evaluate the received event based at least inpart on instructions specified by the continuous query to determinewhether one or more events are to be selected as notable events, andoutput as a result of the continuous query execution.

The continuous query may be programmed using different languages. Incertain embodiments, continuous queries may be configured using the CQLprovided by Oracle Corporation and used by Oracle's Complex EventsProcessing (CEP) product offerings. Oracle's CQL is a declarativelanguage that can be used to program queries (referred to as CQLqueries) that can be executed against event streams. In certainembodiments, CQL is based upon SQL with added constructs that supportprocessing of streaming events data.

In one embodiment, an event processing application may be composed ofthe following component types:

(1) One or more adapters that interface directly to the input and outputstream and relation sources and sinks. Adapters are configured tounderstand the input and output stream protocol, and are responsible forconverting the event data into a normalized form that can be queried byan application processor. Adapters may forward the normalized event datainto channels or output streams and relation sinks. Event adapters maybe defined for a variety of data sources and sinks.(2) One or more channels that act as event processing endpoints. Amongother things, channels are responsible for queuing event data until theevent processing agent can act upon it.(2) One or more application processors (or event processing agents) areconfigured to consume normalized event data from a channel, process itusing queries to select notable events, and forward (or copy) theselected notable events to an output channel.(4) One or more user-defined Java classes may be implemented topartition the notable events based at least in part on attributes of theevent data. In some examples, the even data may then be split up intosub-streams based at least in part on the partitioning, and respectivesub-streams can be sent to appropriate output channels.(5) One or more beans are configured to listen to the output channel,and are triggered by the insertion of a new event into the outputchannel. In some embodiments, this user code is a plain-old-Java-object(POJO). The user application can make use of a set of external services,such as JMS, Web services, and file writers, to forward the generatedevents to external event sinks.(6) Event beans may be registered to listen to the output channel, andare triggered by the insertion of a new event into the output channel.In some embodiments, this user code may use the Oracle CEP event beanAPI so that the bean can be managed by Oracle CEP.

In one embodiment, an event adapter provides event data to an inputchannel. The input channel is connected to a CQL processor associatedwith one or more CQL queries that operate on the events offered by theinput channel. The CQL processor is connected to an output channel towhich query results are written.

In some embodiments, an assembly file may be provided for an eventprocessing application describing the various components of the eventprocessing application, how the components are connected together, andevent types processed by the application. Separate files may be providedfor specifying the continuous query or business logic for selection ofevents.

It should be appreciated that system 200 depicted in FIG. 2 may haveother components than those depicted in FIG. 2. Further, the embodimentshown in FIG. 2 is only one example of a system that may incorporate anembodiment of the present disclosure. In some other embodiments, system200 may have more or fewer components than shown in FIG. 2, may combinetwo or more components, or may have a different configuration orarrangement of components. System 200 can be of various types includinga personal computer, a portable device (e.g., a mobile telephone ordevice), a workstation, a network computer, a mainframe, a kiosk, aserver, or any other data processing system. In some other embodiments,system 200 may be configured as a distributed system where one or morecomponents of system 200 are distributed across one or more networks inthe cloud.

The one or more of the components depicted in FIG. 2 may be implementedin software, in hardware, or combinations thereof. In some embodiments,the software may be stored in memory (e.g., a non-transitorycomputer-readable medium), on a memory device, or some other physicalmemory and may be executed by one or more processing units (e.g., one ormore processors, one or more processor cores, one or more GPUs, etc.) ofthe service provider computers 106 or other computing systems.

FIG. 3 depicts a simplified architecture 300 illustrating additionalaspects and/or features of the custom configuration service 146 of FIGS.1 and 2. For example, FIG. 3 illustrates the custom configurationservice 146 communicatively coupled to an input data stream 302 (e.g.,from one or more streaming sources or channels) and one or more eventsinks 304(1)-(N) (collectively, event sinks 304). As seen in FIG. 3, thecustom configuration service 146 may also be configured with a query 306(e.g., like the query 154 of FIG. 1 which may be received from the user102) for querying the sub-streams (e.g., output of a Java class 308 forprocessing the data of the input stream 302 either after the query 306has extracted notable events from the data stream 302 or to modify thequery 306). In other words, in some examples, the java class 308 may bemodified by the configuration information 155 received from the user 102of FIG. 1 and utilized to implement the query based at least in part onthat configuration information. Additionally, in some examples, when theuser provides, registers, and/or configures the query, they may specifyone or more Java components that are responsible for partitioning thatparticular stream. In any case, the Java class 308 may be configured topartition the data of the input stream 302 based at least in part on theuser-defined configuration information 155 (e.g., based at least in parton attributes of the data stream 302) using an extension of the higherlevel event processing language (e.g., tying a Java implementation witha Java interface to the query language).

In some aspects, when the events are partitioned by the custom Javaclass, each partition may be assigned a partition ID or a bucket ID. Oneor more application programming interface (API) calls or Java interfacecalls may be made to put the partitioned events into buckets based atleast in part on the partition IDs and/or the bucket IDs. These bucketsmay be implemented at runtime and may be based at least in part on ahashing of the partitioned data, where the hashing function may beprovided by the user. In some examples, each partition may be providedto a respective bucket 310(1)-(N) (collectively, buckets 310), which maythen provide the partitioned data to the query 306 via one or moreoutput sub-streams 312(1)-(N) (collectively output sub-streams 312). Insome examples, more than one output sub-stream (e.g., output sub-streams312(1) and 312(2) may provide partitioned data to a query 306, which canmanage the partitioned streams itself and may process them in parallel.Examples of partitions provided to query 306 via the output sub-streams312 include, but are not limited to, IP address from the input stream302 which appeared more than a certain number of times during aparticular period of time, IP address from the input stream 302 whichare associated with an account access (e.g., of the user 102) more thana particular number of times during a particular time period, and/or IPaddress from the input stream 302 with an account name that matches agiven string (e.g., during a period of time, or perpetually). The customconfiguration service 146 may then process the output sub-streams 312(e.g., in parallel) using the query 306 (e.g., by pushing the data ofthe output sub-streams 312 to the query 306) and provide the results tothe one or more event sinks 304. In some examples, the query 306 mayoutput a single stream or relation (e.g., to event sink 304(1))depending on the query construct. However, in other examples, the query306 may provide multiple streams or relations (e.g., one to each ofevent sink 304(1) and 304(N)).

In some examples, the query 306 may be associated with the Java class308 to implement the partitioning by extending the CQL language tosupport a new data definition for a string. The new data definition maybe published in such a way that the partition scheme may be associatedwith the query component with or without the user writing any code.Additionally, each event may then be received from the stream and passedto a Java engine running on the service provider computers 106. In somecases, an XML file may be utilized to associate the Java class 308 withthe query 306 such that the customer configuration service 146 may pickthat up at runtime and while processing the event, invoke the Java class308 to find out which bucket 310 to place the event in, so that they endup in the appropriate sub-stream 312. In order to implement thesefeatures, the CQL language may be modified in order to allow the usersto associate the Java class 308. Additionally, physical operators thatare part of the runtime execution of the CQL engine may be modified toenable the invocation out to the Java class that's being associated.Additionally, each XML file may identify when on what queries toimplement each partition scheme. For example, multiple queries may beassociated with multiple partitioning schemes and each XML file may beable to identify which partitioning schemes belong to which Javaclasses, to which queries, to which streams, and/or to what times. Javaclasses may be reused on multiple different queries and/or data streams.Further, the service provider computers 106 may be configured to providerecommendations and/or suggestions of Java classes to be used fordifferent data streams, business use cases, and/or queries. For example,the event streams may be studied as well as how customers arepartitioning them. As such, one partitioning scheme used by one customermay be recommended to another based at least in part on similar eventstreams, types of data, etc. For example, if it is identified that anevent stream of a first customer includes financial information, and theservice provider computers 106 further identifies that a second customerprocessing financial information has utilized certain partitioningschemes, the service provider computers 106 may recommend that the firstcustomer implement the Java class provided by the second customer and/orthe partitioning scheme used by the second customer.

Additionally, in some examples, the query 306 may be modified based atleast in part on the configuration information 155 received from theuser. As noted, the Java class 308 may be configured to modify the query306 based at least in part on this configuration information 155 inorder to partition the input data stream 302 appropriately. In someexamples, the query 306 may have originally resembled the followingquery statements:

View#1(IPView1):   SELECT ipAddress, acctName    FROM DSLoadChannel1[RANGE 240 SECONDS] Query#1 (DSEventIPQuery1):   SELECT ipAddress FROMIPView1   GROUP BY ipAddress HAVING COUNT(1) = 100 Query#2(DSEventIPQuery2):   SELECT ipAddress FROM IPView1   GROUP BY ipAddress,acctName HAVING COUNT(1) = 50 Query#3 (DSEventIPQuery3):   SELECTipAddress FROM IPView1   WHERE acctName LIKE ‘10101010’ GROUP BYipAddress

In some examples, benchmark data illustrates that the throttle eventsender rate for these queries tends to be around 10,000 events persecond, revealing the following problem areas:

Scalability Issues - CPU Utilization less than 30% - Increasing numberof input threads doesn't scale up the throughput Lack of EffectivePartitioning - Input Thread's run time is ~40% of their life time -Contention among input threads

In particular, regarding a lack of effective partitioning, one issueappears to be that the thread timeline of the benchmark tests shows alot thread contention as the run time of each thread is ˜40% of its lifetime. It appears that this is the case because input data wasn'tpartitioned effectively on IP address by assuming IP address attributeas a “string” value. A few solutions to this problem include, but arenot limited to determining a class (e.g., class A, class B and class C)for each IP address using the IP address octet values, using CQL'sextensibility framework to define IP address as an extensible type whichwill have IP address value as well as its class ID, and defininghashCode( ) method with appropriate hashing mechanism for partitioningthe data according to IP Address classes. For example, using thefollowing code as an example:

public class IPAddress {  public String ipAddress;  // getter & setters @Override  public int hashCode( ){   return ipAddress.hashCode( );  } @Override  public boolean equals(Object obj) {   if(obj instanceofIPAddress) {    IPAddress other = (IPAddress)obj;    returnthis.ipAddress.equals(other.getIpAddress( ));   }   else    returnfalse;  } } As well as: public class DSEvent {  protected IPAddressipAddress ;  protected String acctName ;  protected long partitionId; protected long eventTs ;  protected int hashCode;  public DSEvent ( ) {}  public DSEvent (IPAddress ipAddress, String acctName, long eventTS,long num, long partitionId)  {   this.ipAddress = ipAddress;  this.acctName = acctName;   this.eventTs = eventTS;   this.partitionId= partitionId;   this.hashCode = (int)num;  } ...... ...... }

Regarding the modeling of queries without the current system, one issueappears to be that the usage of database views may not allow forpartitioning at least because in the original queries described above, aview named “IPView1” was defined as follows:

SELECT ipAddress, acctName FROM DSLoadChannel1 [RANGE 240 SECONDS]

However, in the current support of Parallelization using Partitioning,we can't partition a query based on a view source. Thus, the currentdisclosure describes applying the RANGE WINDOW in a query directly asfollows:

SELECT ipAddress FROM DSLoadChannel1[RANGE 240 SECONDS] GROUP BYipAddress HAVING COUNT(ipAddress) = 100Resulting in the following modified queries:

Modified Query 1:   SELECT ipAddress   FROM DSLoadChannel1[RANGE 240SECONDS]   GROUP BY ipAddress HAVING COUNT(ipAddress) = 100 ModifiedQuery 2:   SELECT ipAddress   FROM DSLoadChannel1[RANGE 240 SECONDS]  GROUP BY ipAddress, acctName HAVING COUNT(ipAddress) =   50 ModifiedQuery 3:   SELECT ipAddress   FROM DSLoadChannel1[RANGE 240 SECONDS]  WHERE acctName LIKE‘10101010’   GROUP BY ipAddress

As such, the above techniques and features have provided, among otherthings, a new Java class for IP Address attribute. For example, whilecreating a new event, creating an instance of class IPAddress with IPAddress and its class. Additionally, a mechanism to ensure that a threadin ThrottleEventSender always sends event whose IPAddress belong to sameclass is introduced. Further, separate stat counters for each thread toreduce the critical section are maintained. At least some of thesetechniques have led to improved throttle event sender rates, includinggoing from about 10,000 events per second (as described above) to about50,000 events per second given the same or similar computingenvironments and associated conditions.

Thus, the described techniques provide support of COUNT aggregate fornative OBJECT data type in CQL; fixed Scheduler Thread to send only onetimeout heartbeat per invocation irrespective of downstream processingtime; an introduction of synchronized mechanisms to ensure that notimeout heartbeat propagation takes place if there is an input waitingon input thread; and introduction of a mechanism to transmit a timeoutheartbeat only when the current system time differs from last input'stimestamp by more than timeout value. The described techniques alsoconvert IP Address attribute type (among other attribute types) to a CQLextensible data type by defining it as a JAVA class; ensuring in aThrottle Event sender, that each thread sends events having IP addressesbelonging to same class; and using simple threads instead ofScheduledThreadPoolExecutor threads.

FIG. 4 depicts a simplified flow diagram showing one or more techniques400 for implementing the custom partitioning of data streams, accordingto one example. In FIG. 4, the service provider computers 106 are againshown in communication with the users 102, user devices 104, and/orstream sources 110 via the networks 108. Additionally, in some examples,the service provider computers 106 may include or be in communication(e.g., via the networks 108) with one or more event processor computersand/or databases. While techniques 400 are shown in FIG. 4 in aparticular order (including arbitrary sequence numbers), it should beunderstood that no particular order is necessary and that one or moresteps or parts of the techniques 400 may be omitted, skipped, and/orreordered. In at least one non-limiting example, the one or more serviceprovider computers 106 described above with reference to FIGS. 1 and 2may identify a stream (e.g., associated with a business of the user 102)and/or receive a query from the user for querying events of the stream.In some examples, the service provider computers 106 may also receivecode from the user devices 104. The code may be configured to beexecuted by the service provider computers 106 to interact with orotherwise modify Java code being executed in association with a CEPengine or other even processing server/service/program. The serviceprovider computers 106 may, in some examples, configure the query basedat least in part on the code. The code may identify one or moreparameters of the stream to partition the data by and/or one or moreconditions or rules for partition the data.

In some examples, the service provider computers 106 may also receivestreaming data from the one or more stream sources 110 and thenpartition the results of the stream into buckets and/or sub-streamsbased at least in part on the received code (e.g., at runtime). Asnoted, the partitioning may be based on rules, conditions, and/orattributes of the events themselves as defined by the received code. Insome examples, the service provider computers 106 may then push thesub-streams to the received query for processing (e.g., in parallel) andthen from the query back to the user devices 104 and/or to one or moreevent sinks.

FIGS. 5 and 6 illustrate example flow diagrams showing respectiveprocesses 500 and 600 for implementing the custom partitioning of datastreams described herein. These processes 500, 600 are illustrated aslogical flow diagrams, each operation of which represents a sequence ofoperations that can be implemented in hardware, computer instructions,or a combination thereof. In the context of computer instructions, theoperations represent computer-executable instructions stored on one ormore computer-readable storage media that, when executed by one or moreprocessors, perform the recited operations. Generally,computer-executable instructions include routines, programs, objects,components, data structures and the like that perform particularfunctions or implement particular data types. The order in which theoperations are described is not intended to be construed as alimitation, and any number of the described operations can be combinedin any order and/or in parallel to implement the processes.

Additionally, some, any, or all of the processes may be performed underthe control of one or more computer systems configured with executableinstructions and may be implemented as code (e.g., executableinstructions, one or more computer programs, or one or moreapplications) executing collectively on one or more processors, byhardware, or combinations thereof. As noted above, the code may bestored on a computer-readable storage medium, for example, in the formof a computer program comprising a plurality of instructions executableby one or more processors. The computer-readable storage medium may benon-transitory.

In some examples, the one or more service provider computers 106 (e.g.,utilizing at least the custom configuration service 146 and/or theconfigurable partition module 150) shown in FIGS. 1-4 may perform theprocess 500 of FIG. 5. The process 500 may begin by identifying a queryfor processing a portion of a data stream associated with a user at 502.As noted, the query may be received from the user and may be configuredto query the data stream for real world events. At 504, the process 500may include receiving code that identifies an attribute of theidentified stream. In some examples, the code may be capable ofconfiguring the query based at least in part on an attribute of theevents. The process 500 may also include associating the code with thequery at 506. The code may be associated with the query in manydifferent ways as described above. For example, the code may be utilizedby the service provider computers to modify the query to partition theevent data. The code may also be utilized by the service providercomputers to modify an event processing engine, some JavaScript, orother processes configured to implement the query.

At 508, the process 500 may include receiving event data of the datastream based at least in part on the query. The process 500 may alsoinclude providing each event to a Java class (e.g., based at least inpart on the received code) at 510. The Java class may then beimplemented to partition the events based at least in part on theattribute at 512. At 514, the process 500 may include receiving outputof the Java class implementation (e.g., the sub-streams). The output mayinclude buckets of data that have been partitioned (e.g., each bucketmay correspond to a different partition of the data events). At 516, theprocess 500 may end by configuring the query to process the sub-streamsbased at least in part on the attributes (e.g., further based at leastin part on the implemented Java class).

FIG. 6 illustrates an example flow diagram showing process 600 forimplementing the custom partitioning of data streams described herein.The one or more service provider computers 106 (e.g., utilizing at leastthe custom configuration service 146 and/or the configurable partitionmodule 150) shown in FIGS. 1-4 may perform the process 600 of FIG. 6.The process 600 may begin by identifying a data stream associated with auser (e.g., the user 102 of FIG. 1) at 602. At 604, the process 600 mayinclude determining whether the rate of events being received by thedata stream is above a threshold. In some examples, the threshold mayinclude a number that has been identified as problematic and/or that hasbeen known to create issues (e.g., a low throttle event sender rate, ahigh and/or unmanageable number of input threads, high input thread runtime, input thread contention, starving input threads, etc.). In someexamples, if it is not determined that the rate of events is above thethreshold, the process 600 may include following standard or originalpartition rules and/or query models to provide a single output stream at606. Alternatively, if the threshold is reached at 604, the process 600may include receiving a query from a user at 608. However, in someexamples, the query may be received prior to and regardless of thedetermination at 604.

In some examples, the process may include determining whether a customconfiguration code (e.g., configuration information 155) is received at610. When it is determined that no custom configuration code has beenreceived, the process 600 may include proceeding to 606. However, whencustom configuration code is received at 610, and the rate of events isabove the threshold, the process 600 may include associating the codewith the query at 612. At 614, the process may include passing streamevents to code (e.g., a Java server or engine) for partitioning theevent data based at least in part on attributes identified in the code.At 616, the process 600 may include providing output sub-streams foreach partition/attribute to the query received at 608. Further, theprocess 600 may end at 618, where the process 600 may include processingthe sub-streams using the query (or other query operators including, butnot limited to, filter, aggregate, join, etc.). Thus, after the codepartitions the event stream into sub-streams, downstream queryprocessing may occur.

FIG. 7 depicts a simplified diagram of a distributed system 700 forimplementing one of the embodiments described herein. In the illustratedembodiment, distributed system 700 includes one or more client computingdevices 702, 704, 706, and 708, which are configured to execute andoperate a client application such as a web browser, proprietary client(e.g., Oracle Forms), or the like over one or more network(s) 710.Server 712 may be communicatively coupled with remote client computingdevices 702, 704, 706, and 708 via network 710.

In various embodiments, server 712 may be adapted to run one or moreservices or software applications provided by one or more of thecomponents of the system. In some embodiments, these services may beoffered as web-based or cloud services or under a Software as a Service(SaaS) model to the users of client computing devices 702, 704, 706,and/or 708. Users operating client computing devices 702, 704, 706,and/or 708 may in turn utilize one or more client applications tointeract with server 712 to utilize the services provided by thesecomponents.

In the configuration depicted in FIG. 7, the software components 718,720, and 722 of system 700 are shown as being implemented on server 712.In other embodiments, one or more of the components of system 700 and/orthe services provided by these components may also be implemented by oneor more of the client computing devices 702, 704, 706, and/or 708. Usersoperating the client computing devices may then utilize one or moreclient applications to use the services provided by these components.These components may be implemented in hardware, firmware, software, orcombinations thereof. It should be appreciated that various differentsystem configurations are possible, which may be different fromdistributed system 700. The embodiment shown in the FIG. is thus oneexample of a distributed system for implementing an embodiment systemand is not intended to be limiting.

Client computing devices 702, 704, 706, and/or 708 may be portablehandheld devices (e.g., an iPhone®, cellular telephone, an iPad®,computing tablet, a personal digital assistant (PDA)) or wearabledevices (e.g., a Google Glass® head mounted display), running softwaresuch as Microsoft Windows Mobile®, and/or a variety of mobile operatingsystems such as iOS, Windows Phone, Android, BlackBerry 16, Palm OS, andthe like, and being Internet, e-mail, short message service (SMS).Blackberry®, or other communication protocol enabled. The clientcomputing devices can be general purpose personal computers including,by way of example, personal computers, and/or laptop computers runningvarious versions of Microsoft Windows®, Apple Macintosh®, and/or Linuxoperating systems. The client computing devices can be workstationcomputers running any of a variety of commercially-available UNIX® orUNIX-like operating systems, including without limitation the variety ofGNU/Linux operating systems, such as for example, Google Chrome OS.Alternatively, or in addition, client computing devices 702, 704, 706,and 708 may be any other electronic device, such as a thin-clientcomputer, an Internet-enabled gaming system (e.g., a Microsoft Xboxgaming console with or without a Kinect® gesture input device), and/or apersonal messaging device, capable of communicating over network(s) 710.

Although exemplary distributed system 700 is shown with four clientcomputing devices, any number of client computing devices may besupported. Other devices, such as devices with sensors, etc., mayinteract with server 712.

Network(s) 710 in distributed system 700 may be any type of networkfamiliar to those skilled in the art that can support datacommunications using any of a variety of commercially-availableprotocols, including without limitation TCP/IP (transmission controlprotocol/Internet protocol), SNA (systems network architecture), IPX(Internet packet exchange), AppleTalk, and the like. Merely by way ofexample, network(s) 710 can be a local area network (LAN), such as onebased on Ethernet, Token-Ring and/or the like. Network(s) 710 can be awide-area network and the Internet. It can include a virtual network,including without limitation a virtual private network (VPN), anintranet, an extranet, a public switched telephone network (PSTN), aninfra-red network, a wireless network (e.g., a network operating underany of the Institute of Electrical and Electronics (IEEE) 702.11 suiteof protocols, Bluetooth®, and/or any other wireless protocol); and/orany combination of these and/or other networks.

Server 712 may be composed of one or more general purpose computers,specialized server computers (including, by way of example, PC (personalcomputer) servers, UNIX® servers, mid-range servers, mainframecomputers, rack-mounted servers, etc.), server farms, server clusters,or any other appropriate arrangement and/or combination. In variousembodiments, server 712 may be adapted to run one or more services orsoftware applications described in the foregoing disclosure. Forexample, server 712 may correspond to a server for performing processingdescribed above according to an embodiment of the present disclosure.

Server 712 may run an operating system including any of those discussedabove, as well as any commercially available server operating system.Server 712 may also run any of a variety of additional serverapplications and/or mid-tier applications, including HTTP (hypertexttransport protocol) servers, FTP (file transfer protocol) servers, CGI(common gateway interface) servers, JAVA® servers, database servers, andthe like. Exemplary database servers include without limitation thosecommercially available from Oracle, Microsoft, Sybase, IBM(International Business Machines), and the like.

In some implementations, server 712 may include one or more applicationsto analyze and consolidate data feeds and/or event updates received fromusers of client computing devices 702, 704, 706, and 708. As an example,data feeds and/or event updates may include, but are not limited to,Twitter® feeds, Facebook® updates or real-time updates received from oneor more third party information sources and continuous data streams,which may include real-time events related to sensor data applications,financial tickers, network performance measuring tools (e.g., networkmonitoring and traffic management applications), clickstream analysistools, automobile traffic monitoring, and the like. Server 712 may alsoinclude one or more applications to display the data feeds and/orreal-time events via one or more display devices of client computingdevices 702, 704, 706, and 708.

Distributed system 700 may also include one or more databases 714 and716. Databases 714 and 716 may reside in a variety of locations. By wayof example, one or more of databases 714 and 716 may reside on anon-transitory storage medium local to (and/or resident in) server 712.Alternatively, databases 714 and 716 may be remote from server 712 andin communication with server 712 via a network-based or dedicatedconnection. In one set of embodiments, databases 714 and 716 may residein a storage-area network (SAN). Similarly, any necessary files forperforming the functions attributed to server 712 may be stored locallyon server 712 and/or remotely, as appropriate. In one set ofembodiments, databases 714 and 716 may include relational databases,such as databases provided by Oracle, that are adapted to store, update,and retrieve data in response to SQL-formatted commands.

FIG. 8 is a simplified block diagram of one or more components of asystem environment 800 by which services provided by one or morecomponents of an embodiment system may be offered as cloud services, inaccordance with an embodiment of the present disclosure. In theillustrated embodiment, system environment 800 includes one or moreclient computing devices 804, 806, and 808 that may be used by users tointeract with a cloud infrastructure system 802 that provides cloudservices. The client computing devices may be configured to operate aclient application such as a web browser, a proprietary clientapplication (e.g., Oracle Forms), or some other application, which maybe used by a user of the client computing device to interact with cloudinfrastructure system 802 to use services provided by cloudinfrastructure system 802.

It should be appreciated that cloud infrastructure system 802 depictedin the FIG. may have other components than those depicted. Further, theembodiment shown in the FIG. is only one example of a cloudinfrastructure system that may incorporate an embodiment of thedisclosure. In some other embodiments, cloud infrastructure system 802may have more or fewer components than shown in the figure, may combinetwo or more components, or may have a different configuration orarrangement of components.

Client computing devices 804, 806, and 808 may be devices similar tothose described above for 702, 704, 706, and 708.

Although exemplary system environment 800 is shown with three clientcomputing devices, any number of client computing devices may besupported. Other devices such as devices with sensors, etc. may interactwith cloud infrastructure system 802.

Network(s) 810 may facilitate communications and exchange of databetween clients 804, 806, and 808 and cloud infrastructure system 802.Each network may be any type of network familiar to those skilled in theart that can support data communications using any of a variety ofcommercially-available protocols, including those described above fornetwork(s) 1410.

Cloud infrastructure system 802 may comprise one or more computersand/or servers that may include those described above for server 712.

In certain embodiments, services provided by the cloud infrastructuresystem may include a host of services that are made available to usersof the cloud infrastructure system on demand, such as online datastorage and backup solutions, Web-based e-mail services, hosted officesuites and document collaboration services, database processing, managedtechnical support services, and the like. Services provided by the cloudinfrastructure system can dynamically scale to meet the needs of itsusers. A specific instantiation of a service provided by cloudinfrastructure system is referred to herein as a “service instance.” Ingeneral, any service made available to a user via a communicationnetwork, such as the Internet, from a cloud service provider's system isreferred to as a “cloud service.” Typically, in a public cloudenvironment, servers and systems that make up the cloud serviceprovider's system are different from the customer's own on-premisesservers and systems. For example, a cloud service provider's system mayhost an application, and a user may, via a communication network such asthe Internet, on demand, order and use the application.

In some examples, a service in a computer network cloud infrastructuremay include protected computer network access to storage, a hosteddatabase, a hosted web server, a software application, or other serviceprovided by a cloud vendor to a user, or as otherwise known in the art.For example, a service can include password-protected access to remotestorage on the cloud through the Internet. As another example, a servicecan include a web service-based hosted relational database and ascript-language middleware engine for private use by a networkeddeveloper. As another example, a service can include access to an emailsoftware application hosted on a cloud vendor's web site.

In certain embodiments, cloud infrastructure system 802 may include asuite of applications, middleware, and database service offerings thatare delivered to a customer in a self-service, subscription-based,elastically scalable, reliable, highly available, and secure manner. Anexample of such a cloud infrastructure system is the Oracle Public Cloudprovided by the present assignee.

In various embodiments, cloud infrastructure system 802 may be adaptedto automatically provision, manage, and track a customer's subscriptionto services offered by cloud infrastructure system 802. Cloudinfrastructure system 802 may provide the cloud services via differentdeployment models. For example, services may be provided under a publiccloud model in which cloud infrastructure system 802 is owned by anorganization selling cloud services (e.g., owned by Oracle) and theservices are made available to the general public or different industryenterprises. As another example, services may be provided under aprivate cloud model in which cloud infrastructure system 802 is operatedsolely for a single organization and may provide services for one ormore entities within the organization. The cloud services may also beprovided under a community cloud model in which cloud infrastructuresystem 802 and the services provided by cloud infrastructure system 802are shared by several organizations in a related community. The cloudservices may also be provided under a hybrid cloud model, which is acombination of two or more different models.

In some embodiments, the services provided by cloud infrastructuresystem 802 may include one or more services provided under Software as aService (SaaS) category, Platform as a Service (PaaS) category,Infrastructure as a Service (IaaS) category, or other categories ofservices including hybrid services. A customer, via a subscriptionorder, may order one or more services provided by cloud infrastructuresystem 802. Cloud infrastructure system 802 then performs processing toprovide the services in the customer's subscription order.

In some embodiments, the services provided by cloud infrastructuresystem 802 may include, without limitation, application services,platform services and infrastructure services. In some examples,application services may be provided by the cloud infrastructure systemvia a SaaS platform. The SaaS platform may be configured to providecloud services that fall under the SaaS category. For example, the SaaSplatform may provide capabilities to build and deliver a suite ofon-demand applications on an integrated development and deploymentplatform. The SaaS platform may manage and control the underlyingsoftware and infrastructure for providing the SaaS services. Byutilizing the services provided by the SaaS platform, customers canutilize applications executing on the cloud infrastructure system.Customers can acquire the application services without the need forcustomers to purchase separate licenses and support. Various differentSaaS services may be provided. Examples include, without limitation,services that provide solutions for sales performance management,enterprise integration, and business flexibility for largeorganizations.

In some embodiments, platform services may be provided by the cloudinfrastructure system via a PaaS platform. The PaaS platform may beconfigured to provide cloud services that fall under the PaaS category.Examples of platform services may include without limitation servicesthat enable organizations (such as Oracle) to consolidate existingapplications on a shared, common architecture, as well as the ability tobuild new applications that leverage the shared services provided by theplatform. The PaaS platform may manage and control the underlyingsoftware and infrastructure for providing the PaaS services. Customerscan acquire the PaaS services provided by the cloud infrastructuresystem without the need for customers to purchase separate licenses andsupport. Examples of platform services include, without limitation,Oracle Java Cloud Service (JCS), Oracle Database Cloud Service (DBCS),and others.

By utilizing the services provided by the PaaS platform, customers canemploy programming languages and tools supported by the cloudinfrastructure system and also control the deployed services. In someembodiments, platform services provided by the cloud infrastructuresystem may include database cloud services, middleware cloud services(e.g., Oracle Fusion Middleware services), and Java cloud services. Inone embodiment, database cloud services may support shared servicedeployment models that enable organizations to pool database resourcesand offer customers a Database as a Service in the form of a databasecloud. Middleware cloud services may provide a platform for customers todevelop and deploy various business applications, and Java cloudservices may provide a platform for customers to deploy Javaapplications, in the cloud infrastructure system.

Various different infrastructure services may be provided by an IaaSplatform in the cloud infrastructure system. The infrastructure servicesfacilitate the management and control of the underlying computingresources, such as storage, networks, and other fundamental computingresources for customers utilizing services provided by the SaaS platformand the PaaS platform.

In certain embodiments, cloud infrastructure system 802 may also includeinfrastructure resources 830 for providing the resources used to providevarious services to customers of the cloud infrastructure system. In oneembodiment, infrastructure resources 830 may include pre-integrated andoptimized combinations of hardware, such as servers, storage, andnetworking resources to execute the services provided by the PaaSplatform and the SaaS platform.

In some embodiments, resources in cloud infrastructure system 802 may beshared by multiple users and dynamically re-allocated per demand.Additionally, resources may be allocated to users in different timezones. For example, cloud infrastructure system 830 may enable a firstset of users in a first time zone to utilize resources of the cloudinfrastructure system for a specified number of hours and then enablethe re-allocation of the same resources to another set of users locatedin a different time zone, thereby maximizing the utilization ofresources.

In certain embodiments, a number of internal shared services 832 may beprovided that are shared by different components or modules of cloudinfrastructure system 802 and by the services provided by cloudinfrastructure system 802. These internal shared services may include,without limitation, a security and identity service, an integrationservice, an enterprise repository service, an enterprise managerservice, a virus scanning and white list service, a high availability,backup and recovery service, service for enabling cloud support, anemail service, a notification service, a file transfer service, and thelike.

In certain embodiments, cloud infrastructure system 802 may providecomprehensive management of cloud services (e.g., SaaS, PaaS, and IaaSservices) in the cloud infrastructure system. In one embodiment, cloudmanagement functionality may include capabilities for provisioning,managing, and tracking a customer's subscription received by cloudinfrastructure system 802, and the like.

In one embodiment, as depicted in the figure, cloud managementfunctionality may be provided by one or more modules, such as an ordermanagement module 820, an order orchestration module 822, an orderprovisioning module 824, an order management and monitoring module 826,and an identity management module 828. These modules may include or beprovided using one or more computers and/or servers, which may begeneral purpose computers, specialized server computers, server farms,server clusters, or any other appropriate arrangement and/orcombination.

In exemplary operation 834, a customer using a client device, such asclient device 804, 806 or 808, may interact with cloud infrastructuresystem 802 by requesting one or more services provided by cloudinfrastructure system 802 and placing an order for a subscription forone or more services offered by cloud infrastructure system 802. Incertain embodiments, the customer may access a cloud User Interface(UI), cloud UI 812, cloud UI 814, and/or cloud UI 816 and place asubscription order via these UIs. The order information received bycloud infrastructure system 802 in response to the customer placing anorder may include information identifying the customer and one or moreservices offered by the cloud infrastructure system 802 that thecustomer intends to subscribe to.

After an order has been placed by the customer, the order information isreceived via the cloud UIs, 812, 814, and/or 816.

At operation 836, the order is stored in order database 818. Orderdatabase 818 can be one of several databases operated by cloudinfrastructure system 818 and operated in conjunction with other systemelements.

At operation 838, the order information is forwarded to an ordermanagement module 820. In some instances, order management module 820may be configured to perform billing and accounting functions related tothe order, such as verifying the order, and upon verification, bookingthe order.

At operation 840, information regarding the order is communicated to anorder orchestration module 822. Order orchestration module 822 mayutilize the order information to orchestrate the provisioning ofservices and resources for the order placed by the customer. In someinstances, order orchestration module 822 may orchestrate theprovisioning of resources to support the subscribed services using theservices of order provisioning module 824.

In certain embodiments, order orchestration module 822 enables themanagement of business processes associated with each order and appliesbusiness logic to determine whether an order should proceed toprovisioning. At operation 842, upon receiving an order for a newsubscription, order orchestration module 822 sends a request to orderprovisioning module 824 to allocate resources and configure thoseresources needed to fulfill the subscription order. Order provisioningmodule 824 enables the allocation of resources for the services orderedby the customer. Order provisioning module 824 provides a level ofabstraction between the cloud services provided by cloud infrastructuresystem 800 and the physical implementation layer that is used toprovision the resources for providing the requested services. Orderorchestration module 822 may thus be isolated from implementationdetails, such as whether or not services and resources are actuallyprovisioned on the fly or pre-provisioned and only allocated/assignedupon request.

At operation 844, once the services and resources are provisioned, anotification of the provided service may be sent to customers on clientdevices 804, 806, and/or 808 by order provisioning module 824 of cloudinfrastructure system 802.

At operation 846, the customer's subscription order may be managed andtracked by an order management and monitoring module 826. In someinstances, order management and monitoring module 826 may be configuredto collect usage statistics for the services in the subscription order,such as the amount of storage used, the amount data transferred, thenumber of users, and the amount of system up time and system down time.

In certain embodiments, cloud infrastructure system 800 may include anidentity management module 828. Identity management module 828 may beconfigured to provide identity services, such as access management andauthorization services in cloud infrastructure system 800. In someembodiments, identity management module 828 may control informationabout customers who wish to utilize the services provided by cloudinfrastructure system 802. Such information can include information thatauthenticates the identities of such customers and information thatdescribes which actions those customers are authorized to performrelative to various system resources (e.g., files, directories,applications, communication ports, memory segments, etc.) Identitymanagement module 828 may also include the management of descriptiveinformation about each customer and about how and by whom thatdescriptive information can be accessed and modified.

FIG. 9 illustrates an exemplary computer system 900, in which variousembodiments of the present disclosure may be implemented. The system 900may be used to implement any of the computer systems described above. Asshown in the figure, computer system 900 includes a processing unit 904that communicates with a number of peripheral subsystems via a bussubsystem 902. These peripheral subsystems may include a processingacceleration unit 906, an I/O subsystem 908, a storage subsystem 918,and a communications subsystem 924. Storage subsystem 918 includestangible computer-readable storage media 922 and a system memory 910.

Bus subsystem 902 provides a mechanism for letting the variouscomponents and subsystems of computer system 900 communicate with eachother as intended. Although bus subsystem 902 is shown schematically asa single bus, alternative embodiments of the bus subsystem may utilizemultiple buses. Bus subsystem 902 may be any of several types of busstructures including a memory bus or memory controller, a peripheralbus, and a local bus using any of a variety of bus architectures. Forexample, such architectures may include an Industry StandardArchitecture (ISA) bus, Micro Channel Architecture (MCA) bus, EnhancedISA (EISA) bus, Video Electronics Standards Association (VESA) localbus, and Peripheral Component Interconnect (PCI) bus, which can beimplemented as a Mezzanine bus manufactured to the IEEE P1386.1standard.

Processing unit 904, which can be implemented as one or more integratedcircuits (e.g., a conventional microprocessor or microcontroller),controls the operation of computer system 900. One or more processorsmay be included in processing unit 904. These processors may includesingle core or multicore processors. In certain embodiments, processingunit 904 may be implemented as one or more independent processing units932 and/or 934 with single or multicore processors included in eachprocessing unit. In other embodiments, processing unit 904 may also beimplemented as a quad-core processing unit formed by integrating twodual-core processors into a single chip.

In various embodiments, processing unit 904 can execute a variety ofprograms in response to program code and can maintain multipleconcurrently executing programs or processes. At any given time, some orall of the program code to be executed can be resident in processor(s)904 and/or in storage subsystem 918. Through suitable programming,processor(s) 904 can provide various functionalities described above.Computer system 900 may additionally include a processing accelerationunit 906, which can include a digital signal processor (DSP), aspecial-purpose processor, and/or the like.

I/O subsystem 908 may include user interface input devices and userinterface output devices. User interface input devices may include akeyboard, pointing devices such as a mouse or trackball, a touchpad ortouch screen incorporated into a display, a scroll wheel, a click wheel,a dial, a button, a switch, a keypad, audio input devices with voicecommand recognition systems, microphones, and other types of inputdevices. User interface input devices may include, for example, motionsensing and/or gesture recognition devices such as the Microsoft Kinect®motion sensor that enables users to control and interact with an inputdevice, such as the Microsoft Xbox® 360 game controller, through anatural user interface using gestures and spoken commands. Userinterface input devices may also include eye gesture recognition devicessuch as the Google Glass® blink detector that detects eye activity(e.g., ‘blinking’ while taking pictures and/or making a menu selection)from users and transforms the eye gestures as input into an input device(e.g., Google Glass®). Additionally, user interface input devices mayinclude voice recognition sensing devices that enable users to interactwith voice recognition systems (e.g., Siri® navigator), through voicecommands.

User interface input devices may also include, without limitation, threedimensional (3D) mice, joysticks or pointing sticks, gamepads andgraphic tablets, and audio/visual devices such as speakers, digitalcameras, digital camcorders, portable media players, webcams, imagescanners, fingerprint scanners, barcode reader 3D scanners, 3D printers,laser rangefinders, and eye gaze tracking devices. Additionally, userinterface input devices may include, for example, medical imaging inputdevices such as computed tomography, magnetic resonance imaging,position emission tomography, medical ultrasonography devices. Userinterface input devices may also include, for example, audio inputdevices such as MIDI keyboards, digital musical instruments and thelike.

User interface output devices may include a display subsystem, indicatorlights, or non-visual displays such as audio output devices, etc. Thedisplay subsystem may be a cathode ray tube (CRT), a flat-panel device,such as that using a liquid crystal display (LCD) or plasma display, aprojection device, a touch screen, and the like. In general, use of theterm “output device” is intended to include all possible types ofdevices and mechanisms for outputting information from computer system900 to a user or other computer. For example, user interface outputdevices may include, without limitation, a variety of display devicesthat visually convey text, graphics and audio/video information such asmonitors, printers, speakers, headphones, automotive navigation systems,plotters, voice output devices, and modems.

Computer system 900 may comprise a storage subsystem 918 that comprisessoftware elements, shown as being currently located within a systemmemory 910. System memory 910 may store program instructions that areloadable and executable on processing unit 904, as well as datagenerated during the execution of these programs.

Depending on the configuration and type of computer system 900, systemmemory 910 may be volatile (such as random access memory (RAM)) and/ornon-volatile (such as read-only memory (ROM), flash memory, etc.) TheRAM typically contains data and/or program modules that are immediatelyaccessible to and/or presently being operated and executed by processingunit 904. In some implementations, system memory 910 may includemultiple different types of memory, such as static random access memory(SRAM) or dynamic random access memory (DRAM). In some implementations,a basic input/output system (BIOS), containing the basic routines thathelp to transfer information between elements within computer system900, such as during start-up, may typically be stored in the ROM. By wayof example, and not limitation, system memory 910 also illustratesapplication programs 912, which may include client applications, Webbrowsers, mid-tier applications, relational database management systems(RDBMS), etc., program data 914, and an operating system 916. By way ofexample, operating system 916 may include various versions of MicrosoftWindows®, Apple Macintosh®, and/or Linux operating systems, a variety ofcommercially-available UNIX® or UNIX-like operating systems (includingwithout limitation the variety of GNU/Linux operating systems, theGoogle Chrome® OS, and the like) and/or mobile operating systems such asiOS, Windows® Phone, Android® OS, BlackBerry® 9 OS, and Palm® OSoperating systems.

Storage subsystem 918 may also provide a tangible computer-readablestorage medium for storing the basic programming and data constructsthat provide the functionality of some embodiments. Software (programs,code modules, instructions) that when executed by a processor providethe functionality described above may be stored in storage subsystem918. These software modules or instructions may be executed byprocessing unit 904. Storage subsystem 918 may also provide a repositoryfor storing data used in accordance with the present disclosure.

Storage subsystem 900 may also include a computer-readable storage mediareader 920 that can further be connected to computer-readable storagemedia 922. Together and, optionally, in combination with system memory910, computer-readable storage media 922 may comprehensively representremote, local, fixed, and/or removable storage devices plus storagemedia for temporarily and/or more permanently containing, storing,transmitting, and retrieving computer-readable information.

Computer-readable storage media 922 containing code, or portions ofcode, can also include any appropriate media known or used in the art,including storage media and communication media, such as but not limitedto, volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology for storage and/or transmissionof information. This can include tangible computer-readable storagemedia such as RAM, ROM, electronically erasable programmable ROM(EEPROM), flash memory or other memory technology, CD-ROM, digitalversatile disk (DVD), or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or other tangible computer readable media. This can also includenontangible computer-readable media, such as data signals, datatransmissions, or any other medium which can be used to transmit thedesired information and which can be accessed by computing system 900.

By way of example, computer-readable storage media 922 may include ahard disk drive that reads from or writes to non-removable, nonvolatilemagnetic media, a magnetic disk drive that reads from or writes to aremovable, nonvolatile magnetic disk, and an optical disk drive thatreads from or writes to a removable, nonvolatile optical disk such as aCD ROM, DVD, and Blu-Ray® disk, or other optical media.Computer-readable storage media 922 may include, but is not limited to,Zip® drives, flash memory cards, universal serial bus (USB) flashdrives, secure digital (SD) cards, DVD disks, digital video tape, andthe like. Computer-readable storage media 922 may also include,solid-state drives (SSD) based on non-volatile memory such asflash-memory based SSDs, enterprise flash drives, solid state ROM, andthe like, SSDs based on volatile memory such as solid state RAM, dynamicRAM, static RAM, DRAM-based SSDs, magnetoresistive RAM (MRAM) SSDs, andhybrid SSDs that use a combination of DRAM and flash memory based SSDs.The disk drives and their associated computer-readable media may providenon-volatile storage of computer-readable instructions, data structures,program modules, and other data for computer system 900.

Communications subsystem 924 provides an interface to other computersystems and networks. Communications subsystem 924 serves as aninterface for receiving data from and transmitting data to other systemsfrom computer system 900. For example, communications subsystem 924 mayenable computer system 900 to connect to one or more devices via theInternet. In some embodiments communications subsystem 924 can includeradio frequency (RF) transceiver components for accessing wireless voiceand/or data networks (e.g., using cellular telephone technology,advanced data network technology, such as 3G, 4G or EDGE (enhanced datarates for global evolution), WiFi (IEEE 1402.11 family standards, orother mobile communication technologies, or any combination thereof),global positioning system (GPS) receiver components, and/or othercomponents. In some embodiments communications subsystem 924 can providewired network connectivity (e.g., Ethernet) in addition to or instead ofa wireless interface.

In some embodiments, communications subsystem 924 may also receive inputcommunication in the form of structured and/or unstructured data feeds926, event streams 928, event updates 930, and the like on behalf of oneor more users who may use computer system 900.

By way of example, communications subsystem 924 may be configured toreceive data feeds 926 in real-time from users of social networks and/orother communication services such as Twitter® feeds, Facebook® updates,web feeds such as Rich Site Summary (RSS) feeds, and/or real-timeupdates from one or more third party information sources.

Additionally, communications subsystem 924 may also be configured toreceive data in the form of continuous data streams, which may includeevent streams 928 of real-time events and/or event updates 930, that maybe continuous or unbounded in nature with no explicit end. Examples ofapplications that generate continuous data may include, for example,sensor data applications, financial tickers, network performancemeasuring tools (e.g. network monitoring and traffic managementapplications), clickstream analysis tools, automobile trafficmonitoring, and the like.

Communications subsystem 924 may also be configured to output thestructured and/or unstructured data feeds 926, event streams 928, eventupdates 930, and the like to one or more databases that may be incommunication with one or more streaming data source computers coupledto computer system 900.

Computer system 900 can be one of various types, including a handheldportable device (e.g., an iPhone® cellular phone, an iPad® computingtablet, a PDA), a wearable device (e.g., a Google Glass® head mounteddisplay), a PC, a workstation, a mainframe, a kiosk, a server rack, orany other data processing system.

Due to the ever-changing nature of computers and networks, thedescription of computer system 900 depicted in the FIG. is intended onlyas a specific example. Many other configurations having more or fewercomponents than the system depicted in the FIG. are possible. Forexample, customized hardware might also be used and/or particularelements might be implemented in hardware, firmware, software (includingapplets), or a combination. Further, connection to other computingdevices, such as network input/output devices, may be employed. Based onthe disclosure and teachings provided herein, a person of ordinary skillin the art will appreciate other ways and/or methods to implement thevarious embodiments.

In the foregoing specification, aspects of the disclosure are describedwith reference to specific embodiments thereof, but those skilled in theart will recognize that the disclosure is not limited thereto. Variousfeatures and aspects of the above-described disclosure may be usedindividually or jointly. Further, embodiments can be utilized in anynumber of environments and applications beyond those described hereinwithout departing from the broader spirit and scope of thespecification. The specification and drawings are, accordingly, to beregarded as illustrative rather than restrictive.

1. A system, comprising: a memory storing computer-executableinstructions; and a processor configured to access the memory andexecute the computer-executable instructions to at least: identify adata stream associated with a user; receive a query for processing atleast a portion of the data stream; identify a rate of events of thedata stream, the rate of events comprising a number of events receivedby the processor per time period; determine that the rate of events isabove a threshold; and when the rate of events is above the threshold:provide, to the user, a software development kit for generating a javaclass; enable the user to create a partition class in java using thesoftware development kit, the partition class configured to: receiveeach event of the data stream at runtime; and partition the data streaminto a plurality of sub-stream portions; receive, from the user, anattribute of the data stream; receive, from the user, the partitionclass generated by the user for partitioning the data stream, thepartition class based at least in part on the software development kitand the identified attribute of the data stream; associate, by thecomputer system, the partition class with the query; process each eventof the data stream with the partition class at runtime to partition thedata stream based at least in part on the association of the partitionclass with the query; the process comprising: partitioning the datastream into the plurality of sub-stream portions based at least in parton the attribute; provide at least one sub-stream portion of theplurality of sub-stream portions to the query; process the at least onesub-stream portion, using the query, in parallel with at least a secondsub-stream portion of the plurality of sub-stream portions that isreceived as an output from the partition class; and provide results ofthe query based at least in part on the at least one sub-stream portionand the second sub-stream portion.
 2. The system of claim 1, whereinassociating the partition class with the query comprises tying the queryto the partition class.
 3. The system of claim 2, wherein the processoris further configured to execute the computer-executable instructions toat least: receive an event of the data stream based at least in part onthe query; provide the event to the partition class; and receive anoutput from the partition class.
 4. The system of claim 3, wherein thedata stream is partitioned based at least in part on the output from thepartition class.
 5. The system of claim 1, wherein the attributecomprises at least one of a first identifier of a computing device of acustomer of the user, a second identifier of an item associated with theuser, or a third identifier of an item attribute associated with theitem.
 6. The system of claim 5, wherein the identifier of the computingdevice of the customer indicates an Internet Protocol address of thecomputing device of the customer or a geographic region in which thecomputing device of the customer is located.
 7. A non-transitorycomputer-readable storage memory storing a plurality of instructionsexecutable by one or more processors, the plurality of instructionscomprising: instructions that cause the one or more processors toidentify a query for processing at least a portion of a data streamassociated with a user, instructions that cause the one or moreprocessors to identify a rate of events of the data stream, the rate ofevents comprising a number of events received per time period;instructions that cause the one or more processors to determine that therate of events is above a threshold; and when the rate of events isabove the threshold, the plurality of instructions further comprise:instructions that cause the one or more processors to provide a softwaredevelopment kit to the user; instructions that cause the one or moreprocessors to enable the user to create a partition class in java usingthe software development kit, the partition class configured to: receiveeach event of the data stream at runtime; and partition the data streaminto a plurality of sub-stream portions; instructions that cause the oneor more processors to receive, from the user, an attribute of the datastream; instructions that cause the one or more processors to receive,from the user, the partition class generated by the user forpartitioning the data stream, the partition class identifying anattribute of the data stream and generated based at least in part on thesoftware development kit; instructions that cause the one or moreprocessors to process each event of the data stream with the partitionclass at runtime to partition the data stream based at least in part onan association of the partition class with the query, the processing ofeach event comprising: partitioning the data stream into the pluralityof sub-stream portions based at least in part on the attribute; andinstructions that cause the one or more processors to configure thequery to process at least one sub stream portion of the plurality ofsub-stream portions in parallel with at least a second sub-streamportion of the plurality of sub-stream portions that is received as anoutput from the partition class; and instructions that cause the one ormore processors to provide results of the query based at least in parton the at least one sub-stream portion and the at least one secondsub-stream portion.
 8. (canceled)
 9. (canceled)
 10. The non-transitorycomputer-readable storage memory of claim 7, wherein the plurality ofinstructions further comprise: instructions that cause the one or moreprocessors to receive an event of the data stream based at least in parton the query; instructions that cause the one or more processors toprovide the event to the partition class; instructions that cause theone or more processors to implement the partition class to process theevent based at least in part on the attribute; and instructions thatcause the one or more processors to receive an output from the partitionclass.
 11. The non-transitory computer-readable storage memory of claim10, wherein the output from the partition class is at least one of theone or more sub-streams.
 12. The non-transitory computer-readablestorage memory of claim 7, wherein the attribute is specified by theuser prior to receipt of the partition class.
 13. The non-transitorycomputer-readable storage memory of claim 7, wherein at least onesub-stream of the one or more sub-streams is generated based at least inpart on a hashing function performed on the at least a portion of thedata stream.
 14. A computer-implemented method, comprising: identifying,by a computing system, a data stream associated with a user; receiving aquery for processing at least a portion of the identified data stream;identifying a rate of events of the data stream, the rate of eventscomprising a number of events received by the computing system per timeperiod; determining that the rate of events is above a threshold; andwhen the rate of events is above the threshold: receiving, from theuser, a partition class in java identifying an attribute of theidentified data stream, the partition class configured to: receive eachevent of the data stream; and partition the data stream into a pluralityof sub-stream portions based at least in part on the attribute;executing the partition class at runtime to partition the data streaminto at least one sub-stream portion of the plurality of sub-streamportions based at least in part on the attribute; processing the atleast one sub-stream using the query in parallel with at least a secondsub-stream portion of the plurality of sub-stream portions that isreceived as an output from the partition class; and providing results ofthe query based at least in part on the at least one sub-stream portionand the second sub-stream portion.
 15. The computer-implemented methodof 14, further comprising associating the code partition class with thequery.
 16. (canceled)
 17. (canceled)
 18. The computer-implemented methodof claim 14, wherein the data stream is partitioned by the partitionclass.
 19. The computer-implemented method of claim 14, wherein thesub-stream portion is generated based at least in part on a hashingfunction performed on at least one of the plurality of sub-streamportions.
 20. The computer-implemented method of claim 14, wherein theattribute comprises at least one of an Internet Protocol addressassociated with a customer of the user or a geographic region associatedwith the customer of the user.